# Three of the objects are made of identical gray paper while one is made of a different shade. - PowerPoint PPT Presentation

Three of the objects are made of identical gray paper while one is made of a different shade.

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Three of the objects are made of identical gray paper while one is made of a different shade.

## Three of the objects are made of identical gray paper while one is made of a different shade.

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1. PERCEPTUAL STRATEGIES FOR MATERIAL IDENTIFICATIONQasim Zaidi Rocco RobilottoByung-Geun KhangSUNY College of Optometry

2. Three of the objects are made of identical gray paper while one is made of a different shade. • Illumination on the right is half the illumination on the left. 1 2 3 4 What is the number of the odd object?

3. “3” is the number of the odd object! 3 1 2 4 What strategy did you follow?

4. Method of constant-stimuli • Brightness discrimination threshold from % side-correct • Lightness identification threshold from % object-correct

5. PREDICTIONS FOR TWO MODELS

6. Results of Lightness Identification Hypothesis: P(I|D)=1.0 Rejected at p=0.01 Chi-squared > 16.8 Note systematic asymmetry in lightness identification compared to brightness discrimination.

7. Choose the object that is most different in brightness. • Disregard the material and the illumination difference. 1 2 3 4 Brightness is different from photometer luminance because of adaptation and lateral interactions.

8. Results of Brightness Dissimilarity Note systematic asymmetry in curves is similar to lightness identification results.

9. Brightness Discrimination Thresholds (Reflectance units) ST SS u Full Illum. l Half Illum. Lightness Identification and Brightness Dissimilarity Thresholds (Reflectance units) ST SS

11. Lightness identification based on brightness dissimilarity responses of adaptation model k k k

12. PERCEPTUAL STRATEGIES FOR LIGHTNESS IDENTIFICATION • Under everyday conditions, observers consistently judge and remember surfaces as having a certain lightness or grayness. • This phenomenology is not sufficient evidence that the visual system generically has access to the lightness of materials. • For 3-D objects, we show conditions where lightness identification is limited solely by the limen of brightness discrimination, and other conditions where lightness identification is considerably worse. • More importantly, we show that the same relative brightness based strategy reproduces both sets of results.

13. Differences between brightness and color information for material identification

14. Systematic changes in cone-coordinates

15. Algorithms for simultaneous identification for Lambertian and non-Lambertian 3-D objects

16. Three of the filters are identical while one is different. • Illumination on the right is skylight, on the left is sunlight. Which is the odd filter?

17. FILTER IDENTIFICATION ACROSS ILLUMINANTS Identical filters will cause systematically different spectral changes in two spectrally different lights. Given information about the filtered and unfiltered lights, can an observer identify two filters with identical spectral transmittances?

18. 36 EVERYDAY MATERIALS (VRHEL et al 1994) SDs of Material Chromaticities Means of Material Chromaticities • Changes in means are systematic across illuminants. • Filters can increase or decrease the color variation.

19. Three of the filters are identical while one is different. • Illumination on the right is skylight, on the left is sunlight. Which is the odd filter?

20. SIDE CORRECT & FILTER CORRECT CURVES • Method of constant stimuli • DELTA is distance between standard and distractor. • 6 standards x 5 tests x 2 daylights x 6 deltas x 10 reps

21. FILTERS IDENTIFICATION VS DISCRIMINATION

22. RESULTS Identification thresholds were generally similar to discrimination thresholds and were significantly greater only in 5%, 10%, 13%, and 23% of 60 pairs for 4 observers. Observers can identify filters across different illuminants almost as well as they can discriminate filters within the same illuminant.

23. MATERIAL IDENTIFICATION ACROSS ILLUMINANTS X-junctions at the edges of the circular filters, in conjunction with the systematic changes in chromaticities of partially overlaid objects, promote a percept of transparency. Are these results influenced by transparency. By rotating the image of the circular filtered regions by 180, we abolished the edge-based cues to transparency while leaving unchanged the figural and color contents inside and outside the filters.

24. Three patches are from the same material while one is different. • Illumination on the right is skylight, on the left is sunlight. Which is the odd material?

25. MATERIALS IDENTIFICATION VS DISCRIMINATION

26. FILTERS VS MATERIALS DISCRIMINATION THRESHOLDS IDENTIFICATION THRESHOLDS

27. RESULTS Disrupting the X-junctions and color relations at the edges of the filters did not appreciably affect discrimination or identification. Systematic color changes at the edges of the filter and X-junction cues to filter transparency were not necessary for good discrimination or identification performance.

28. COLOR IDENTIFICATION ACROSS ILLUMINANTS • Both experiments can be conceived as identification of a circular ensemble of materials across the two natural lights. • Both experiments indicate that identification of ensembles of materials is almost as good as discrimination between ensembles of materials. • The significant cues are the chromaticity shifts across illuminants.

29. CORRELATED CHROMATICITY CHANGES • L/(L+M) coordinates translate • S/(L+M) coordinates scale • Means can be used to estimate translation and scaling constants • Simple algorithms can exploit the affine transforms for material identification

30. PAIRS OF CONFUSED FILTERS Means of Material Chromaticities Are RED and BLUE confused because observers judge deviations from directions of color vectors better than lengths of color vectors?

31. Color identification across illuminants within the same scene • If the material statistics are constant, then color identification only requires comparing rank-orders of chromaticities. • If material statistics change, affine transforms will give correct identification, but rank orders will not.